Categorization Effects in Value Judgments: Averaging Bias in Evaluating Combinations of Vices and Virtues
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
How do consumers evaluate combinations of items representing conflicting goals? In this research, the authors examine how consumers form value judgments of combinations of options representing health and indulgence goals, focusing on how people estimate the calorie content of such options. The authors show that when evaluating combinations of healthy (virtue) and indulgent (vice) options, consumers tend to systematically underestimate the combined calorie content, such that they end up averaging rather than adding the calories contained in the vice and the virtue. The authors attribute this bias to the qualitative nature of people's information processing, which stems from their tendency to categorize food items according to a good/bad dichotomy into virtues and vices. The authors document this averaging bias in a series of four empirical studies that investigate the underlying mechanism and identify boundary conditions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it